Keywords without the Adwords Keyword Tool
The AdWords keyword tool is the starting point for many search engine marketers, both organic and paid. Even so, it is not the only source of insight into how users search and what words they use. In my latest guest post on Search Engine People, I outline a few alternative sources for keyword information:
Google’s products dominate the search industry in many ways, but there are few tools more ubiquitous than the AdWords Keyword tool. Its data is incorporated in any number of SEO tools, including those offered by SEOmoz, Majestic SEO and Raventools.
Bing's updated webmaster tools,
With Bing’s updated webmaster tools, including better keyword tools, there is no reason not to. There is more to search than just Google, and I don’t just mean Bing or Yahoo! either. See what I mean in Keywords without the Adwords Keyword Tool.
Rank Changes by Position in Google
Raw Rank Change to Position (Figure 1)
How much can you expect a search rank in Google to change? Is dropping by five places worth panicking about? What if it happened on the fifth page, or should you only worry if it were on the first? Being able to make decisions based on this kind of information is important in managing workflow within most Search Engine Optimisation (SEO) projects.
It is easy to assume that you will always observe changes of greater range the further away from the first position in Google you get. Especially as the further away from the first spot you get, the greater the rate at which you can climb to the top.
Ranking data is available and easy to get through a number of services and tools, with SEOmoz, Raven Tools, Advanced Web Ranking (AWR) and Google Webmaster Tools able to manage and automate much of the process.
Plotting Search Engine Result Page Rank Changes
Looking beyond just the week-to-week changes requires a little more work. And data. Most good tools provide historic data for keywords that are currently being tracked, and this is the data used for this post. The ranking data was collected over a period of time from multiple sites and across multiple keywords in Google using SEOmoz.
This data was used to create the sample seen in Figure 1, plotting the observed position on Google’s SERPs and how much it changes from the next week’s rank. In the scatter plot, Change is on the y axis, while Current Rank is on x.
| Google.AU Current (x, Week 1) | Week 2 | Change (y) |
| 2 | 5 | 3 |
Chart 1
The points in Figure 1 represent Google.AU Current, and Change, a number derived from the difference between the current rank and the next observed rank for that keyword. Consequently, when Change is a positive number, it represents a movement away from the top position, while a negative number represents movement towards it.
As SEOmoz collects ranking data for tracked keywords appearing in the first 50 positions in Google, Bing and Yahoo, the only changes in ranking tracked have to fall within this range (however there are other tools that provide data beyond the first 50). This limit affects the data, skewing the range of change lower than what is probably seen within the population, especially as the rank gets closer to 50 and to 1. Due to these constraints, the data used in this post is a truncated distribution, restricted to observing only changes between 1 and 50.
Range of Change per Position
Standard Deviation per Position with Mean (Figure 2)
The standard deviation for each rank position seen in Figure 2 from the data used is fairly inconsistent, and not entirely unexpected given the limited nature of the data. The size of the sample used does not seem to be large enough to provide enough observations at each rank.
Four Bins
| Q | Range | Cases | Cumulative | Change Mean | Change Standard Deviation |
| 1 | 1 | 25.2% | 25.2% | 0.34 | 3.08 |
| 2 | [2, 5] | 31.8% | 57.0% | 0.38 | 3.11 |
| 3 | [5,13] | 18.1% | 75.1% | 0.38 | 3.81 |
| 4 | [13,50] | 24.9% | 100.0% | -0.52 | 4.84 |
Chart 2
The sample is heavily weighed towards the first ten positions, with very little data available for any rank beyond the second page of Google (Figure 3), giving an inter-quartile range of just 2 to 13 from 1 to 50. The first two quartiles range from first to fifth position, and the third only just reaches the second page of Google’s search results.
Quartile Ranges (Figure 3)
Even within these limitations, it can easily be shown that there is a difference in expected movement as a site’s position falls further down the rankings. Figure 4 displays the distribution of individual observations, standard deviation of change per quartile with the black error bars, and the mean of change with a 95% confidence interval in red.
Standard Deviation of Change by Quartile (Figure 4)
Using quartiles produces a series of standard deviation of change closer to what you would expect: a great range of observed changes in rank as you move away from position one. While the data supports the hypothesis, the range of positions covered by the last 25% is too large relative to the sample population to be meaningful.
k-means Clustering
Another approach taken with the data was k-means clustering, with k as four clusters. More than four clusters failed to break up the one to six range, accounting for about 63% of observations, and reduced the number of observations in the other groupings below a useful level. Even at four clusters, the groups outside the one to six range never accounted for more than 17% of the sample.
| k | Range | Skewness | Cases | Change Mean | Change Standard Deviation |
| 1 | [19,34] | -1.412 | 11.912% | 0.204 | 4.335 |
| 2 | [33,50] | -4.012 | 8.915% | -1.488 | 5.856 |
| 3 | [7,18] | 1.542 | 16.120% | 0.004 | 3.046 |
| 4 | [1,6] | 10.147 | 63.053% | 0.408 | 3.337 |
Chart 3
Looking at the skewness across each of the clusters seems to prove that the further down from one a position goes, the more it skews towards going up. Unfortunately for cluster 1 and 2, this is a deceptive number. As there are no observations outside of the top 50, the closer you get to 50, the fewer drops in rank will be observed, rendering the data biased towards increases in rank.
Unfortunately this was inevitable. As seen in the quartile ranges, the positions between one and five accounted for at least 57% of all observations. This distribution of data is an artefact of how the sample was created, with the keywords selected by non-random means.
k-means Distribution (Figure 5)
It is certain to be a product of the limitations of the data collected, where the only observations included must be changes in position occurring between 1 and 50. Unsurprisingly, the same tendency towards a greater range of change the further away from the top of Google can be seen within the k-means clusters.
k-means Cluster Standard Deviation of Change (Figure 6)
Much like Figure 4, Figure 6 includes black error bars representing one Standard Deviation from mean, and red error bars for a 95% confidence interval of mean for each cluster. The clusters are not in order of the positions in Google they represent:
| k | 1 | 2 | 3 | 4 |
| Range | [19,34] | [33,50] | [7,18] | [1,6] |
Chart 4
The data in Chart 4 revealed that the range of change increased from cluster 1 to cluster 2. These two groups were both represented by the last 25% of all observations, or the final group in Chart 2. k-means clustering can also highlight outlier populations within a data set.
k = 6 Clusters (Figure 7)
Partitioning the sample data into six clusters highlighted one group of observations within the first ten positions. This group showed a significantly higher than average change in rank compared to other values in this range. This group is also reflected in the skewness of cluster 4 in Chart 3.
Making Sense of the Data
There are a number of issues with the sample used for this blog post. These limitations mean that the data presented here is not a good selection of the query spaces in which the sites used exist. A few of the problems include:
- Only 1400 records were used
- Massive convenience sampling issues such as:
- Keywords are selected by inconsistent, non-random criteria
- SEOmoz data has no visibility past 50, which limits ability to observe changes involving any rank beyond that point
- No differentiation between keywords such as taxonomy or competitiveness
- No allowance for known algorithm changes
Convenience sampling is a significant issue with the data selected. Tracking terms selected for campaign and client management is certainly best practice from an SEO perspective. The data collected will create a false impression of how search engines behave in a broader sense, and only provide insight into one of the search environments as defined by the objectives of those involved. It is almost certain that this will focus the sample on vanity and short/head terms, with little tracking of long tail queries.
The data SEOmoz collects is a truncated distribution with no visibility on behaviour past 50. In practical terms, this means that the highest change it is possible to observe in this set is either 49 or -49. Terms dropping down to below 50 are not included in the data set, nor are terms coming up from below this rank.
Even within these limitations, the data did demonstrate an increase in average rate of change either up or down. Unfortunately the sample was not large enough, nor did it cover enough of a range to provide any heuristics for most of the positions observed.
New Auction Insights into AdWords Competitors
Google announced a new report for AdWords on the Inside AdWords blog, and I wrote a quick post covering my first impressions. The post went live on Aspedia’s own blog, the company where I work, and covers a few interesting points.
Yesterday the Inside AdWords blog posted “Make smarter decisions with the new Auction insights report”, announcing the release of the new Auction insights report. This new report supplies information on who is competing for certain keywords and how aggressively…
One of the more interesting things about this new report is that it provides Impression Share at the keyword level, something that has not been seen before. The new Auction Insights Report also provides some interesting competitor data. Another statistic provided that is worth returning to in-depth is the Top of page rate.
Average Position as reported in AdWords (and Webmaster Tools) has been a rather unclear metric. An average by itself without any other summary statistic or standard deviation is not very useful. However reporting Average Position as well as the percentage of impressions for which the ad appeared in the top three spots at least provides some clue as to the distribution of the observations the average is calculated from. It is certainly a topic worth looking at further.
You can read the post, New Auction Insights into AdWords Competitors, here.
You can find the ROI of a Telephone
Apparently you cannot put trust and love in a spreadsheet. According to a number of people who attended SXSW’s panel on ROI for Social Media, this makes tracking the return on investment of Social Media for business impossible. During the panel the #SXSMROI Twitter stream was full of comments following this theme. Many commented that Social Media’s value for business shouldn’t be measured in terms of money, either the loss, saving or acquisition thereof.
The most surprising thing about the sentiment expressed during the panel showed how little the attitude towards Social Media has changed within business. Much of the visible discussion about ROI for Social Media is focused on arbitrary “value of a fan” figures, engagement, conversation and raw fan, like, retweet and follower metrics. Revenue and cost rarely get discussed.
What is the ROI of Trolls?
Return on Investment is actually a very straightforward concept. An old textbook from university defines it as “a ratio of required costs and perceived benefits of a project or an application” (King, Lee & Viehland, 2004 p. 569). At its simplest you measure what goes into a project or business process, be it cash, time spent by employees and other resources, and compare that to what the business gets out of it. It is practically gamification, where businesses keep score on what what is working and what is not by the numbers in the bottom line.
Finding the ROI of a Telephone
One of the persistent memes dogging the discussion compares finding the ROI for Social Media to establishing a return for using now ubiquitous, common technology. Kind of like this remark seen bearing the #SXSMROI hastag:
Asking if there is ROI for Social Media is like asking if there is an ROI of the telephone or a pencil.
It is a witty statement and fits into Twitter’s 140 character limit. It is also wrong. You can find the ROI of a telephone, or a pencil for that matter. Service and technology companies such as Intel (PDF) think so. Aside from the savings of switching to a VoIP system, Intel’s case study also outlined another business benefit for adopting their systems: productivity gains. The return on a project is not always in creating a new revenue stream.
The worst part of the analogy quoted above is that practically every piece of technology in common use now at some point had to be shown to have value. From the phone, to mainframe computers, desktop machines, mobiles and the Internet.
Business systems often provide more benefits to the bottom line than just directly generating revenue or cutting costs. In many cases it is how these tools create efficiencies internally or assist in acquiring and serving customers that creates the return for the business.
Meet Ms Revenue and Mr Expenditure
There are two things fundamental to running a business and ROI; revenue and expenditure. Most organisations attempt to link expenditure to a source of revenue, either directly such as in the case of marketing, or indirectly in the case of business functions such as customer service and HR.
A successful, sustainable business
The one simple, inescapable fact at the centre of this discussion is that Social Media projects cost money. Either as cash for supporting marketing, tools and external experts, or the money spent on the wages of those involved with posting content, creating cat-themed memes and responding to customers.
Just don't cross the streams
It is important that posting cat photos and building love and trust contributes to the business’ bottom line. Knowing what works for the business makes it possible to decide if the marketing department should spend their time drafting tweets or creating another eDM. It means knowing if customer service staff should stick to the phones, or be trained to respond to questions on Facebook. Pouring money and resources into projects with no return is not a sustainable practice, no matter how many Twitter followers a brand might gain or the number of happy Facebook fans who have won an iPad.
Finding the Return on Social Media
Tools like Google’s Social Reports in Analytics and Assisted Conversion funnels do make it easier to track the effect of a Social Media campaign on online activity and sales even if they don’t reveal much about other channels. However measuring the return on engaging in Social Media for business is not limited to last click attribution and cross channel sales tracking.
Social Media is more than just another marketing channel. Because of the nature of the platforms, an organisation’s Social Media spaces will inevitably be used by the community as they see fit. Social Media sites such as Pinterest and Facebook have more in common with shared public spaces than dedicated media channels.
One of the more common ways an organisation’s audience appropriate these spaces is to expedite customer service and to make general enquiries. In fact a number of very successful Social Media initiatives have taken advantage of this behaviour, such as Dell with @DellCares.
Because of the ease of disseminating information and the access they can give to a pre-existing audience, Social Media is a natural fit for Business Communication and a powerful way to serve customers directly. These functions often do not directly generate revenue but they still create value for the business.
For example, while I was with Greyhound Australia the brand’s Facebook page was often used to respond to product questions and customer service enquiries, in addition to normal promotional activity. By responding to questions in public in a shared space the brand was able to communicate with the person who ask as well as others with the same question.
Under normal circumstances using online spaces to address customer queries helped to improve customer service in general, it was during extraordinary events where it created the most value. During the Queensland floods Greyhound’s online spaces, including Facebook, were important for keeping customers informed, and managing direct customer enquiries. It made it possible to communicate with customers directly and en masse.
Using online tools such as the company’s website and Social Media spaces made it easier for customers to find the information they needed to be aware of changes to services and manage the load experienced in other customer service channels. While this is an extreme example, it was an expansion of existing practices in response to an extraordinary situation. Facebook was already used as a communications and customer service channel as well as a marketing tool, and the return on the time and resources invested were measured as such.
Love, Trust, Engagement and Staying in the Black
Social Media projects for business will be treated like any other. Goals will be set, processes put in place and KPIs assigned. Until someone discovers a way to pay for servers with love and trust, some form of economic benefit will be expected, which in turn will be weighed against the costs associated with the project.
The idea that Social Media is different really should be dead and buried by now. Developing and managing online communities takes effort, and in a commercial setting, this costs money. Likes and retweets don’t pay bills, and at some point, people need to get paid.
King, D, Lee, J & Viehland, D 2004. Electric Commerce; A Managerial Perspective, Pearson Education, Upper Saddle River, New Jersey.
Getting the right audience
Like Facebook, LinkedIn offers advertisers a lot of choices for selecting the perfect audience. Advertising can be targeted by general demographic information to job titles, groups and even the company the potential audience is employed by. Sometimes with so much choice, choosing which tools not to use can be as important as selecting those that you do. You can read the full version of Getting your LinkedIn Audience Right over at Search Engine People now.
Advertising on LinkedIn has more in common with advertising on Facebook than AdWords. Like Facebook, LinkedIn allows its advertisers to target ads based on demographic and personal information, and not current activity such as search. AdWords is more about the task the user is currently engaged in, with a few other variables available to limit reach and limit the ads to a more potentially productive audience.
Read the full version here.
